Goal : extract the same statistics as HexaLab to compare the computation of the Scaled Jacobian over the same hexahedral mesh

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px

Open CSV file

These files are generated with test_scaled_jacobian. See ../app/test_scaled_jacobian.cpp for more information

CSV_file = '../data/S1/SJ.csv'
df = pd.read_csv(CSV_file)

df.head(10)

	hex index	minSJ
0	0	0.999866
1	1	0.967752
2	2	0.845130
3	3	0.855850
4	4	0.613585
5	5	0.844899
6	6	0.641436
7	7	0.587914
8	8	0.695953
9	9	0.985510

Some stats

print("Min = ",round(df['minSJ'].min(),3))
print("Max = ",round(df['minSJ'].max(),3))
print("Avg = ",round(df['minSJ'].mean(),3))
print("Var = ",round(df['minSJ'].var(),3))

Min =  -0.492
Max =  1.0
Avg =  0.923
Var =  0.017

Histogram

Compute the bins

bins = pd.DataFrame()
bins['count'], bin_edges = np.histogram(df['minSJ'], bins=100, range=(0,1))
bins['value_min'] = bin_edges[:-1]
bins['value_avg'] = 0.5 * (bin_edges[:-1] + bin_edges[1:])
bins['value_max'] = bin_edges[1:]
bins

	count	value_min	value_avg	value_max
0	0	0.00	0.005	0.01
1	0	0.01	0.015	0.02
2	5	0.02	0.025	0.03
3	1	0.03	0.035	0.04
4	0	0.04	0.045	0.05
...	...	...	...	...
95	293	0.95	0.955	0.96
96	373	0.96	0.965	0.97
97	487	0.97	0.975	0.98
98	786	0.98	0.985	0.99
99	4443	0.99	0.995	1.00

100 rows × 4 columns

How to use the same bins as HexaLab ?
also 100 bins but weird ticks values

Plot with matplotlib (static)

#recomputing the histogram
#df['minSJ'].plot(kind='hist',bins=100, xticks=np.arange(0,1,0.1), xlim=(1,0), figsize=(16,8))

#using the bins dataframe
bins_sorted = bins.sort_values('value_avg',ascending=False)#eq to flip x axis
bins_sorted['value_avg'] = bins_sorted['value_avg'].round(3)
ax = bins_sorted.plot.bar(x='value_avg',y='count',figsize=(16,8),rot=90)
#remove some x labels
for i, t in enumerate(ax.get_xticklabels()):
    if (i % 10) != 0:
        t.set_visible(False)

Plot with Plotly (interactive)

#recomputing the histogram
#fig = px.histogram(df['minSJ'], nbins=100)

#using the bins dataframe
fig = px.bar(x=bins['value_avg'], y=bins['count'], labels={'x':'Scaled Jacobian', 'y':'count'})
fig.update_xaxes(range=[1, 0])#flip x axis
fig.show()